There is so much data these days; some say it's just too much. In the field of healthcare, big data promises to reduce expenses and improve quality care. Financial services companies want to increase customer touch points, increase the value of their client portfolios, and reduce risk. Consumer packaged goods (CPG) companies want to (for one) explore how their supply chain can be streamlined and shifted to decrease manufacturing costs and ultimately increase revenue.

Most companies think that creating a huge repository of data for historical analysis is a measure of Big Data success. The reality, however, couldn't be further from this. Instead of growing such a repository, Big Data should focus on how to utilize the data collected in order to find out prescriptive solutions for problems they face.

There are three elements to Big Data - Volume, Variability and Velocity. The volume of data that gets captured in data warehouses is only part of the whole picture. Companies have increasingly complex business processes in a highly dynamic business environment. In this article, we'll explore how prescriptive analytics can help companies manage variability of business processes and high velocity of change to achieve financial gains. The focus of companies is essentially to make money - Big Data's ROI can be increased significantly by focusing on the right business problems and adopting prescriptive analytics.

Gain ROI from Big Data Initiatives

There has been tremendous increase in adoption of IoT sensors, smart devices, cloud computing devices etc. This has resulted in increased knowledge of the behaviors and habits of consumers, customers, employees, patients etc. The promise of Big Data is better informed, insightful and reasoned decision making - the more data we collect and analyze, the greater the potential positive impact of the decisions we make.

There is huge opportunity in making a strategic business decision based on recommended actions derived from past performance, current conditions and desired outcome. Most of the focus in Big Data analytics, however, hasn't been desired outcome - it has focused on analyzing past performance and extrapolating it to future performance.

Prescriptive Analytics: Going from What and When, to Why and How

Analytics have advanced from providing rudimentary descriptions of the past, to insightful analysis of what is likely to occur in the future, to now recommending specific actions to create predictable outcomes.

Prescriptive analytics focuses on this last order of analytics, prescribing actions based on desired outcomes; given specific scenarios, past and current events. The ability to manage outcomes through prescribed actions both increases the effectiveness of decision makers and manages risk in the decision-making process. Strategic decisions can now be based not only on what has or is likely to occur in the future, but also through prescribed actions based on why and how things occur to create desired outcomes. Below are two real-life use cases that illustrate the value that prescriptive analytics has brought to organizations.

But whether it's a government organization, a process manufacturing company or even a retail business, prescriptive analytics can be leveraged to make the most informed, sound decisions for any industry.

Sales Operations and Planning

Typically used by manufacturers, S&OP used to be largely a process supported by spreadsheets. Eventually, as organizations began to grow in complexity, systems of record came along to support the process. Their goal has been primarily to establish "one version of the truth" by helping users plan and collaborate against one set of data.

Next generation S&OP are incorporating prescriptive analytics to help users find the best version of the truth. Recently named by Gartner as a market category, prescriptive analytics helps organizations uncover hidden sources of value. It also helps identify risk to better react to unplanned events. The use of prescriptive analytics in S&OP helps identify previously unforeseen insights, increase business agility and establish a better grasp on performance predictability.

Healthcare: Integrated Delivery Network Optimization

Most healthcare provider organizations run separate processes to make key decisions. Clinical guidelines establish patient care rules and guidelines. The process helps determine business models, which markets to compete in, and the level and type of service to be offered.

The budgeting process is largely concerned with financial objectives and results in budget and employee constraints for each department and location. Asset planning (usually done in spreadsheets) helps determine the use of space, beds and equipment. Finally, workforce planning tools help create schedules to ensure key personnel will be available when and where needed.

Imagine a prescriptive analytics platform that sits on top to capture the essence of each process. The clinical rules and guidelines establish the baseline requirements for patient care, including treatments, staff qualifications, equipment, supplies, and metrics such as length of stay. Strategy guidelines are reflected in the form of objectives and targets. Financials are represented in the form of costs and constraints, such as the firm's budget, cash flow or borrowing capacity. Assets are reflected including ED capacity (PODs, rapid assessment zones, etc.), floors/wards (number of beds), operating rooms, etc. Each asset is linked to the clinical guidelines and includes activity definitions such as throughput for different types of patients as well as yields such as complication rates. Finally, key staff is represented including physician types, nurses, technicians, etc. All activity, staff and assets are tied to costs. Finally, revenue is represented explicitly either as government funding or tied to activity.

Big Data: Looking to the Future

Organizations of all kinds are embracing opportunities to pull value from Big Data and advanced analytics. Early efforts in advanced analytics helped organizations understand their business and predict future activities based on historical data, business rules and known activities. Advances in computing have enabled prescriptive analytics - a higher order of advanced analytics to prescribe actions in order to create desired outcomes and mitigate risk.

Prescriptive analytics enables organizations across almost any industry to increase profitability, improve market competitiveness and create market opportunities as a result of better-informed business decisions through the use of Big Data.